1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for new version of accumulate_n op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.eager import backprop
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.platform import test
class AccumulateNV2EagerTest(test_util.TensorFlowTestCase):
"""Tests of the new, differentiable version of accumulate_n."""
def testMinimalEagerMode(self):
forty = constant_op.constant(40)
two = constant_op.constant(2)
answer = math_ops.accumulate_n([forty, two])
self.assertEqual(42, answer.numpy())
def testFloat(self):
np.random.seed(12345)
x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)]
tf_x = ops.convert_n_to_tensor(x)
self.assertAllClose(sum(x), math_ops.accumulate_n(tf_x))
self.assertAllClose(x[0] * 5,
math_ops.accumulate_n([tf_x[0]] * 5))
def testGrad(self):
np.random.seed(42)
num_inputs = 3
input_vars = [
resource_variable_ops.ResourceVariable(10.0 * np.random.random(),
name="t%d" % i)
for i in range(0, num_inputs)
]
def fn(first, second, third):
return math_ops.accumulate_n([first, second, third])
grad_fn = backprop.gradients_function(fn)
grad = grad_fn(input_vars[0], input_vars[1], input_vars[2])
self.assertAllEqual(np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1
[elem.numpy() for elem in grad])
if __name__ == "__main__":
ops.enable_eager_execution()
test.main()
|
